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    "# Stock Price Forecasting with LSTMs\n",
    "\n",
    "In this tutorial we show how a simple LSTM can predict stock prices in the short term. \n",
    "\n",
    "A [Long-Short Term Memory](https://en.wikipedia.org/wiki/Long_short-term_memory) (LSTM) is a type of Recurrent Neural Network that is specially suited for time-series prediction problems. It is internally composed by two kind of units, hidden units, which as in [CNNs](https://miguelgfierro.com/blog/2016/a-gentle-introduction-to-convolutional-neural-networks/) contain a hidden representation of the inputs, and gated units, which control the amount of information that flows from the inputs, to the hidden units and to the outputs.  \n",
    "\n",
    "The gated structure of LSTMs, make them very efficient when working with large time series. They are able to understand long-term dependencies in temporal data, controlling which information should be \"remembered\" and which \"forgotted\". \n",
    "\n",
    "In the present tutorial we use [Keras](https://github.com/keras-team/keras) with Tensorflow back-end to predict the stock prices of Microsoft from 2012 to 2017. We experiment with an LSTM and two variants of the same family: Bi-directional LSTM and [GRU](https://en.wikipedia.org/wiki/Gated_recurrent_unit). "
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      "Using TensorFlow backend.\n",
      "/Users/miguel/anaconda3/envs/py35/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: compiletime version 3.6 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.5\n",
      "  return f(*args, **kwds)\n"
     ]
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     "name": "stdout",
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     "text": [
      "OS:  darwin\n",
      "Python:  3.5.4 |Anaconda, Inc.| (default, Nov  8 2017, 18:11:28) \n",
      "[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "Keras:  2.1.5\n",
      "Tensorflow:  1.5.0\n",
      "Numpy:  1.14.1\n",
      "Number of CPU processors:  8\n"
     ]
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import math\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Bidirectional, LSTM, GRU\n",
    "import tensorflow as tf\n",
    "from bokeh.io import output_notebook\n",
    "from utils import (get_number_processors, plot_series, to_1dimension, plot_series_prediction)\n",
    "\n",
    "print(\"OS: \", sys.platform)\n",
    "print(\"Python: \", sys.version)\n",
    "print(\"Keras: \", keras.__version__)\n",
    "print(\"Tensorflow: \", tf.__version__)\n",
    "print(\"Numpy: \", np.__version__)\n",
    "print(\"Number of CPU processors: \", get_number_processors())\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data\n",
    "\n",
    "The data we are going to use is Microsoft stock prices from 2012 to 2017. The data can be accessed directly in [Yahoo finance](https://finance.yahoo.com/quote/MSFT/history?p=MSFT).\n",
    "\n",
    "The parameter `TIME_AHEAD` sets the number of days that you want to predict in advance. The more in the future you want to predict, the less accurate the prediction will be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = os.path.join('data', 'MSFT_2012_2017.csv')\n",
    "EPOCHS = 5\n",
    "TEST_SIZE = 0.3\n",
    "TIME_AHEAD = 1 #prediction step\n",
    "BATCH_SIZE = 1\n",
    "UNITS = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1509, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>26.549999</td>\n",
       "      <td>26.959999</td>\n",
       "      <td>26.389999</td>\n",
       "      <td>26.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>26.820000</td>\n",
       "      <td>27.469999</td>\n",
       "      <td>26.780001</td>\n",
       "      <td>27.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>27.379999</td>\n",
       "      <td>27.730000</td>\n",
       "      <td>27.290001</td>\n",
       "      <td>27.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-06</td>\n",
       "      <td>27.530001</td>\n",
       "      <td>28.190001</td>\n",
       "      <td>27.530001</td>\n",
       "      <td>28.110001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-09</td>\n",
       "      <td>28.049999</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>27.719999</td>\n",
       "      <td>27.740000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Open       High        Low      Close\n",
       "0  2012-01-03  26.549999  26.959999  26.389999  26.770000\n",
       "1  2012-01-04  26.820000  27.469999  26.780001  27.400000\n",
       "2  2012-01-05  27.379999  27.730000  27.290001  27.680000\n",
       "3  2012-01-06  27.530001  28.190001  27.530001  28.110001\n",
       "4  2012-01-09  28.049999  28.100000  27.719999  27.740000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(DATA)\n",
    "df = df.drop(['Adj Close', 'Volume'], axis=1)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To simplify the problem, we are going to take as input the average of the Open, High, Low and Close values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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95PaHZxkD6NoV1qypPMNMlybefASPq+oBwAuq2k5V24ZelgSMMVXO2LFuopmBA2Ho0GB/s2IauTduXDWajkL8JwIAVPVqETkE8KtIPlfVGckNyxhjUm/hQrfs1s0tr7gC6tevOl/4JSk1EYjI9cCVgD8lw6siMlxVI/clMMaYymCdN7RmY68WdPjw9MWSSqUmAuBy4DequglARIYC/yOBTmXGGFMZbNoEu+0G1VI1+E6GiPLPFWBnaHsnsRXHxhhTJWzcCHXrpjuK1IvyRPAiMFlE3sElgL7A80mNyhhjUki9gW42bcrORFDqE4GqPoKbs/hnYA0wqKwdzIwxJtPMmOGKgjp0gLVrq8ZooomKMvrovsBsVX0cmAkcJSINkx6ZMcaU07JlcPPNsHVryedcfrlbLloE779fdYaNSESUOoK3gJ0i0h7XyWxv4LWkRmWMMRXgqafg0Ufhn/+M3b9rl0sOy5a58YPC9tsvZeFljCiJYJeq7gDOBv6uqrcCzZMbljHGVJyff3ajhf70k9u+/36oUwf22cdt33cffPwxnH8+DBuWvjjTJUpl8XYR6Q9cApzh7auRvJCMMaZi1Knjlps2QZs2sHy5Gx5i6tTY8y68ENq3d0NLZKMoTwSDgB7Ag6r6nYi0BV5JbljGGFN+ft3AF1+4JABFk8CHH7okkM2itBqaA9wCzBSRTkC+qg4t5TJjjEk7f5axL74oeuzJJ11x0SmnpDamTBSl1dCxwALgSeAp4FsROTrJcRljTEIKCtwooR98EOxbvDj2nK1b4dRT4c474Zpr3OTzJlodwV+Bk1R1PoCI7IebcezQZAZmjDGJePxxN7/waacFHcQWLAiO77031KoVmyiMEyUR1PCTAICqfisilkeNMRllzJhgff16qFkTliwJ9o0alfKQKo0oiSBPRJ4D/uVtXwTkJS8kY4xJzP/+55p/+hqGurzeey907w5duqQ8rEojSiK4GrgWuN7b/gJXV2CMMRlh/vySjw0YAO1sKq244iYCEcnBzVB2EfBIakIyxpjE+EVAixfDMcfA99+71kD77GNJIIq4iUBVd4rIPiJSU1ULUhWUMcYkYvFiaN0a2raF776DefPgoIPSHVXlEaVoaDEwUURGAZv8nd6opMYYk3aLFgW//HNyLAkkKkrP4kXAaO/ceqGXMcakVYFXTrF4sRUBlUeUyevvK+vNveGqnwM6AQoMBuYDrwNtgCXA+aq6tqzvYYzJDrNnu9Y/33zjOo5NmgQ9e7rWQCtWwL77pjvCyitKz+JPw/MPiEgjEfk43jUhjwEfqer+wCHAXOB2YKyqdgDGetvGGBPXG2/A5s3w7LMwc6YbYhpg2jS3tCeCsotSNNRUVdf5G96v9z1Lu0hEGgBH401rqaoF3n36Ai95p70EnJlo0MaY7PX3v0PnzvDqq7H77Ymg7KIkgp0i0trfEJF9cMU8pWkLrAZeFJGpIvKciNQFmqmqNw4gK4BmiQZtjMk+S5e65bZtxR+3RFB2URLBEGCCiLwiIv8CPgfuiHBddaAb8LSqdsW1OIopBlJVpYSkIiJXikieiOStXr06wtsZY6oyf1KZsIsvDtYbN05dLFVNlGGoP8J9ob8OjAQOVdUodQT5uCGrJ3vbb3r3WSkizQG85aoS3ne4quaqam7Tpk0jvJ0xpiorKHD9BML8GcZM+ZSYCERkf2/ZDWgN/Oi9Wnv74lLVFcD3ItLR23U8MAcYBQz09g0E3itz9MaYrLF9uxtBNGzPUmsrTRTxmo/+HrgCNwx1YQr0inD/3wGvikhNXMe0Qbjk84aIXAYsBc5PKGJjTFYqKIDddovd16YNvPsu7LVXWkKqMkpMBKp6hbc8rqw3V9VpQG4xh44v6z2NMdmpoAAaNICRI6FfP7evfXs48MD0xlUVlJgIROTseBeq6tsVH44xJpusWgXNvHaDn3wCN97oppUMV/xu2gTLlrlEULMmXHCB60fw4IPWd6CixKssfhO4Ezjde50Rep2e/NCMMVXdyJHB+kknwZw58PLLbvvRR2GPPdxgcgce6DqT1azpjj3wgNuuXTv1MVdF8eoIzgb6AZ1xFbojVHVhSqIyxmQFv1dw2E03wWGHwc03x+5fuDBIBCJQp07y48sWJT4RqOq7qtoPOAY38NxfRWSCiByTsuiMMVXaqlWuorduXejQIdh/5JHFnz9nTmriyjZROpRtBdYDG4DdAXsYM8aU27ZtkJ8PXbu6OYbnz4epU4uet//+wforr6QuvmwSrx9BLxEZDkwBjgMeU9UuETuTGWMqkYIC0CgDx1SQb75x5fvTp0O3bm4OARE3kuhvfhOc9/DD7hyA00+P7UlsKk68J4IxQHdgAlALuEREHvdfKYnOGJN006ZBrVrw2mtlu/73v4fRoxO75v33g/UBA2KP+b2Hb70VbrnF1Qv8+GNsxbKpWKIl/AwQkYHFHvCo6kvxjlek3NxczcvLS9XbGZM17roLnnjCFc307es6ZyVi27ag5U7UJwpVqFYtdjvshx/g8svd00CnTonFY2KJyBRVLa4vV4x4HcpS9kVvjEmtn3927fAfCU04W1CGWcm/+y7xa8Kjhxb3JNGyJXz4YeL3NWUXZc5iY0wVc845MH587L4aNaJfv2OHmxegYcPSzy3MTzgPPQSnnZb49abiWSIwJsuoFk0CEIz3H8Xtt8NfC41CpuoqfEvjJwLrDJY54rUaGuotz0tdOMaYZPv55+L3T5/ueusCPP00fBynfeB//1t0365d0d5/+3a39DuHmfSL12qot4gI0SahMcZksLlz4bbbYOtWWLIk9lirVsH62rXunGuugfOLGRd4zRo39MP69UWP7dgRLRb/icASQeaIVzT0EbAW2F1ENgCCG35acJOL1U9BfMaYCnDmmfDtt3DAAXDVVW7f/fe7ZpzjxgXDO69ZA7vv7tY3bCh6n7vuck8L4MYG+uST4NiOHa4ZamksEWSeeENM3KqqDYH3VbW+qtYLL1MYozGmHO6+2yUBgEGDgi/i666DL790Y/a85LURXLs2fuuhX34J1lu0cOcPHeq2oz4RvPOOW1a3GsqMUep/ClXtKyLNgMO8XZNV1SYRNqYSKChwI3UWdtVVsS1+OnrzCG7eHJThFyfc5v+aa9w9/ErfqIngttvcctmyaOeb5Ct1rCGvsvgr4DzcbGJfici5yQ7MGFN++fnF73/mmdgWPn7R0KZN0Z4ILrvMjRAKwS/7KIkgXNxUlqanJjmiPJzdCRymqqsARKQpbviJN5MZmDGm/KI2CfUTQbwngp07YcYMOPlkGD482O8ngp07S38f/ylg8GCXTExmiDL6aDU/CXjWRLzOGJNmK1a4Ze/eQdn83LlFz6tb1y0HDoxNHgUFsGCBGyTuvvtci6MLL4wdIqKkJ4KhQ4s2Qf3+e7e87LLYe5j0ivJE8JGIfAyM8LYvAD5IXkjGmIri/7p//HHYd9+SxwNq1ChYHzEiWB8/3j0BhIWHhYbiE8Hy5a7TGbgKZb8YyE8Ee+8d+Z9gUqDUnKyqtwL/wM1U1hkYrqq3JTswY0z5+V/OpbXQqVULTjnFrYfH/C+cBKDoPMH+vcNFSn5LIgiaq4IrGqpWDZo3jx+PSa1IDbi8ieptsnpjKhm/3D4np/Rz33/fJYR4rYbAzSMc5pf7P/AA/Otfbv2xx4Lj337rBprbvt09EbRsaU1HM42V0hlThSWSCKpVi94ENKxvX7fMz4effnLJwG+RdNVVrs7h0EOhXj03WqkVC2UeSwTGVGGJJIKwQYOK319cz+EDDnBNSWvXdkVCAwa4uogLL3TFSGvXwuzZ7twvvoAGDRKLxSRfpEQgInVEpGOiNxeRJSIyU0SmiUiet6+xiHwqIgu8ZaPS7mOMKZtEE4E/vMQxxxQ9dsUVMGtW8dftuSesXBl7vHHj4H5hfv8DkzmidCg7A5iGG3sIEekiIqMSeI/jvLmO/VlybgfGqmoHYKy3bYxJAj8RRC2Tb9LELdu0Cfb16uVGGx0+HNq3L/66Zs1cIvB/+YOrG/D7J4TddVe0WEzqRHkiuBc3d/E6AFWdBrQtx3v2BfzZz14CzizHvYwxcST6RLDPPm65775BOf+hh8Jxx8W/rn5912TUbx4K7nq/f4LviCOsojgTRUkE21W18KCzEWcnRYFPRGSKiFzp7Wumqsu99RVAs4j3MsYkyK/8jZoI3ngDXn7ZDU3t9zloFKHw9qefgvV//xtOPRVee63oE0GU0UlN6kVJBLNF5EIgR0Q6iMgTwKSI9z9SVbsBpwLXisjR4YOqqpSQVETkShHJE5G81attjDuTHqpw7LHB6JyVTaJPBM2aucresDVrSr+uZctg/Zxz4IMPXB1Bhw6x722JIDNFSQS/Aw4CtuF6F28Aboxyc1X9wVuuAt7BFTGtFJHmAN5yVQnXDlfVXFXNbdq0aZS3M6bC/fwzfPYZXHppuiMpm7K2GgorqTdy2D33wCOPwLp1sYPZ7befm8TmhRfctiWCzBSlZ/FmVR2iqod5X8xDVHVradeJSF0RqeevAycBs4BRwEDvtIHAe2UP35jkWrgweffevh0mTYr2RVtWfiIoz7g+xc1UVlidOnDTTcU3Da1bN0gANhlNZiq12kZExlFM8Y2q9irl0mbAO262S6oDr6nqRyLyNfCGiFwGLMUNbW1MRjr88GB97Fh4/XX3y7e4ZpGJevVV117/5ZddcUrt2hU/ENvOne5pIMqk8oWtWuW+wOtXwDRUfumuPdxnpij197eE1msD5wCl9j9U1cXAIcXsXwMcHzVAY9Kl8Ly8J5zglt26uXH7V66Ehx4q+/39oRneegsuuQSGDIE//rHs9yuOnwjKoiK/tM87D557Dm65pfRzTepFKRqaEnpNVNWbgWOTH5ox6eUPxzxsWOz+NWvcF9rDDwe/dMvCb2kzerRblieplKQ8iaAitWgBM2dC2/I0PDdJE6VDWePQaw8RORmwTuKmyluyxC2POip2vz/GP8R2oErUypVu6ZfjR5nYBdwUkVF7527bZu32Temi/C8yBVdHILgioe8Am1vIVHn+hO/t2sHRR8Pnn7tf15NCjae/+cYNpnbooYnf308Evl27XFn+0qXQunXJ1z39dLT7b93qJqMpPGy0MYVFKRpqq6rtvGUHVT1JVSekIjhjUuH+++E//ym6/6uv3FALe+zhhmj+4gs3Zv833wTn/P73kJvrJnN57rnE3rdwIvD5vXtL88EHJRdNjR/vWvJ8/33sfADGFEe0hLZrInJ2vAu9OQpSIjc3V/Py8lL1diZL7NgBTzwBN9/stgv/KbRp41oNjRwZ7Hv/fTj99JLvmUhT0MaN3cicxdm82X2Rh7VrB927u5ZLvr594d13i16/335uikmInSHMZBcRmRIa561E8YqGzohzTLGJakwld+ut8Le/BdvLlwczZy1d6l5XXBF7zWmnxb9nIpWz69a5oRzy8+Hcc+HNN4Njl14K/frBGWe4Mv7vvgteYe+V0Atnyxa3zM21JGBKV2LRkKoOivManMogjUmGsWNjt9u3d8VEmzcHo2/GG14hPAuXL2qF765d7unh/PPdkM9/+pMr6vnDH9zxN96As8+GG70+/B99VPx9iuvAtXatSy63317032hMcaLOR3CaiPyfiNztv5IdmDHJ9sMPrkWQ/yW7ebMbKiH8K7tLl5KvP/LIovuizvDlTwfZpIkrz+/QwQ3UdtNNsef5QzMU7uG8ZInrhLZ+vWsZFOY/NfzmNxXTGcxUfVGajz4DXIAbc0iA84CI1VnGZKadO90v5+OOcxO0jxkTHAs/BRQegC3skENcJXHh+0ZR0qTyhecD3rIFpk93xVZh++zjBsODoh3fNm50y4ro/WyyQ5QngiNU9RJgrareB/QA9ktuWMYkz+efu3l1VYMv3uOPh/vuc+t+P4Fnn40/NENOjivHX7YsmNEr6hOBf16NGkWP+R3MfBMnwrRpRX/d+5XJfn2Ab9Mmt7REYKKKkgj8/802i0gLYDvQPHkhGVMxVF2LoK5dg9Y5L73kvrT90UTDE6lff71b+r++S5pbd9YsePHFYHvvvV1lLySeCIrr7HXaaa4p6pdfumQzYwbMnQt33OEquF95xZ3Y34i1AAAYuklEQVRXWiIoPCmMMSWJ0qFstIg0BB4GvsG1GHo2qVEZUwGmTYNHH3XrY8e6L+vhw2PP2X//YL1ePbf0O5KV1NrmoIPcK8z/Qk+0jqC4JwKAy7wum3vtFcyFcOSRsfUS/qQvhRPBL7+4pSUCE1WpiUBVH/BW3xKR0UDtYmYsMybjhNv0z53rlosXw8UXu+GQ69SBjh2Dc3Jy3JfrhAnuiz2RSdYTTQTxngjCfvghWM8t1Bq88BNBXh7Mm+cqlqtXd01TjYkiyjDUM4CRwOuqugg3QY0xGS/8S/nrr127/RUr4OCD4f/+r/hr6tVzrYc6dkys/b3/he5XFs+ZA4MHw8cfF1/EFDURHHccjBvnhqeuXTv2WOFEEE5cDRrY2P8muih1BGfgxhh6Q0S+FpFbRCTOSCjGZAb/C7J9e1fe7jfB3C9OUwe/uKWk+oGShJ8ICgqgVy+YPNk18SxOvMrisDFj3BNMuE7C53/Rf/xx0SeRwsVFxsQTZayhpar6kKoeClwIdMYNPGdMRtu82S179XJj8nz6qdved9+Sr/Fb2iSaCPzexDt2uEpnfxyhKVOKH3bCryMo7YmgWjVXOXzJJUWP+Unk4YeLJpTCTw/GxBO1Q9k+IvJ/uCKi/YESHqyNyRzhRABBr914Y+KfeKJbhiuRowg/EYR7AfvFUYVFLRqKJ97ThD+JjjFRRKkjmAzUAN4AzvNmHjMm423Y4Jbh6SYvuCB++3r/l3q4WWkU4UTQsKEbp+jf/3Yzcz37rOuh/OGH8OST7lf+1Knu/ESfPMKKSwQLF7p/g1UUm0RE+T1yiarOT3okxlSwdevcskkTN73kN9/ARRfFv2bPPd1y167E3sv/Uv7qK9cT+MILg/qB6dPdFJTbt7vxf/bZxw0rAWWbx6Dwe/ruuit+sZcxJYnSfNSSgKmU1q1zZfd167rK4o8+ij+ENLixftavLzrqaGn8Hsr+2P8HH+x6JV92mSvj9580fvzRJYIVK1xHt0aNEnufsMKJ4I47yn4vk90i1REYU1ls2uRa2Nx4o5sUvl0794Vco4Yb0jnekBHgksbDDyc+WFvzQn3tb73VLfv0ca2IfD/+6J4aPvyw6BhBiSqcCArPX2BMVDabqalSBgxw0zP6TjklNe+7117B+qOPBq2ICk85mZcXDEexuJy1beFEMHly+e5lslukRCAiRwBtwuer6stJismYGPfcA506uYrXklxyCXzySdBss2tXVyFbeOL5ZKlVK1gPl/s3bhx73l/+Eqz361e+9wwngu7dy3cvk92itBp6BdgXmAb4g+wqYInAJJ2qmyzGXy+JPxAbuF/abdu6RNC5c3LjK064xVGTJsF6tWpBJfRXX5U/Nr9DWYsW5buPMVHqCHKBnqp6jar+zntdH/UNRCRHRKZ64xQhIm1FZLKILBSR10XEOsKbEhWe4H31ajjrrNj911wTrNevH/QT6No1+rSRFcH/0m/WLNhXt64b6fSRR4IEceaZbjiI8FNEWey2Gzz1lBum2pjyiJIIZgF7lXpWyW4A5oa2hwKPqmp7YC1wWTnubaqwyZNjK2G3bHGjh777bjCqKMDTTwfr8YqPkm3mTFc/UbjS9sUXXWukxx932z/+WHHvefXVwbSaxpRVlDqCPYA5IvIVoQHnVLVPaReKSCvgNOBB4GYREaAXbqgKgJeAe4Gni72ByWq33Ra77Y8DBLBqVdERNgcNcr+Q06V5c/drvyS9e7tE9dvfpi4mY6KIkgjuLcf9/4YbjsIb6Z0mwDpV9YfIygdaluP+pgqLV/btD8K2dKlbtmnjmn1m8oib1au7SemNyTRROpR9VpYbi8jpwCpVnSIix5bh+iuBKwFaF26DZ7JCeCz+eP71r9J7DBtjShZl8vrDveGnN4pIgYjsFJENEe7dE+gjIktwg9X1Ah4DGoqIn4BaAcX+uavqcFXNVdXcpk2bRvrHmKolP98Vt7z1luuNC64p6ZNPBueMHOnGDzLGlF2UyuK/A/2BBUAd4HLgybhXAKp6h6q2UtU2QD/gv6p6ETAO8LrUMBB4rwxxmypu82b47ju48ko4+2x47DG3/7zz3ETzOTnwj3+4JFCeETyNMRE7lKnqQhHJUdWdwIsiMhUo68gmtwEjReSPwFTg+TLex1QCO3e6mboOPhhuuSX6dcuXu34D7dq57b59Ydu2oA5gy5bSJ3UxxkQTJRFs9tr6TxORh4DlJDhGkaqOB8Z764sB6weZJc48E0aPduu//W38IaDBdbh6+OFgcvjwdJHhimBLAsZUnCiJYADui/864CZgb6CECfiMcaZPd809//vfYN/ixfF7006YUHRIiPKMzmmMiSbSVJWAAM1V9T5VvVlVFyY/NFNZDRniJmLZbz9X1u8PyLZkScnXqBY/LpB1ljIm+aK0GjoDN87QR952FxEZlezATOW0di386U+x+447zi0XLnTDPpx9dnCsoMBN0uJPI+n7z3/g3nttpi1jUkE03khegIhMwTX9HK+qXb19M1X14BTEB0Bubq7m5eWl6u1MOSxbFjT19K1f737Zt2rlhmHwz6tVCw44AH7+OTj3yitdpXKHDikL2ZgqS0SmqGpuaedFqfTdrqqFp9CInz1M1tq0yS1793bLO+5wA8F17RokAYBx49y0iuEkMGSIaxJqScCY1IpSWTxbRC4EckSkA3A9MCm5YZnKyk8Egwe79v5XX+22e/WKrTj+3e9g48bYa/2WQsaY1IryRPA74CDcgHMjgA3AjckMyqTPli2uvD78Sz0RfiJo2BBuvjkYibNnT7c87TT3JLChmL7pnTqV7T2NMeUTpdXQZlUdoqqHeUM+DFHVrakIzqTe22/Dn/8Md96Z+LVffQXPe90Dw2PyAxx5pBs6+pVXYM89i79+v/0Sf09jTPmVWDRUWsugKMNQm8xWUACzZ7umnv6k7tOnu+W6dYnf7ze/Cdb9yWF81au7CeUBatcO9v/zn67YaM6c8k/UYowpm3h1BD2A73HFQZNxfQlMFbFxI9TzBgf/4gv3ix3cr3pwLX3K6qqr3MxcJXn3XWjQwK0PHOiW4ekdjTGpFa9oaC/gD0An3KihJwI/qepnZR2a2mSG226LnUv3qKNcW36A7793yx07ilwWd87gjz92yx494Jln4r9//fquHuKvf40csjEmiUpMBKq6U1U/UtWBwOHAQmC8iFyXsuhMhfvhB3joIVcsFHbccXDqqSUnghtucJOvF2fVKjjlFLc+b160OB580FUmG2PSL27zURGphZtqsj/QBngceCf5YZlk8Wf0AtfxK7z90UfBeuFE4M+3u3Zt0fF/xo4N1teurZg4jTGpU+ITgYi8DPwP6Abc57UaekBVI84bZTLRsmXB+t13u7qA8AifAIccAtu3x+7zz8nPL3rPRYsqNkZjTGrFqyO4GOgA3ABMEpEN3uuXiDOUmQzz88/BwG9XXAH9+rny+n79gnP23NPNFVz4iaB+fbdcsybYp+qGhLjrrmDeAGNM5ROvjqCaqtbzXvVDr3qqWj+VQZryW7PGVRDfcQe0bAnDh7thoiGoL7jzTlfGX716yYngk0/csmNHGDAAnn3WbXfr5uYO/vLL5P9bjDEVK6EJZkzl5fcPADdbWNg55wTLRo3cpC+Fi4b85p1//rNrYfTtt/Dqq8Hxxo3dBPLhvgTGmMrBZnsF8vJcxyq/TXtV9HxoQtDCk8P07u2++P25f8NPBOvWuXqBnJzg/Iceir2+Uye45JKKj9kYkxr2RID7Irz0Ujcejl/0sW0brF6d1rAq1AcfBOv771/0eHgC+HAi8OcbnjEjOP7hh8H6zTe7UUX9sYSMMZVPVieC2bPdF5n/hT9pEpx8smtLf911ruL0P/9xE7BXZuvWudd998GwYa4IJ57q1YOioffec8twayOAp592ScIfXdQYU3llddHQCSfAihVF948cCV9/7db79IG//x2uvTa1sVWkIUPcsmNHuOCC0s8Xcf0LmjRxk8kX58gj3WT0xpjKL2ufCFRjk8Cf/hRUmn73nRsq2ffaa6mNraL5Qz60bx/tfL/Ct/BQ1I0bB+stWpQ/LmNMZsjKRFBQEDtcwtdfu2aVb77pOlO9/74bjrlmTTj3XFi+vGLed/ly6Ns3tiduKpx1lhvZ89BDo51/9dVw4IHB9qWXuqX/ZAFFexcbYyqvrEwEk0Lzq+XnQ25oRs/+/YPy8CZN3JPBd98FQyiH5eUVHbMnbPNmV+ms6oqXWrSAUaPg1lsr5t8R1cqVbjC4RHz4ofucVOHFF93y8stdAujTJxi22hhT+SUtEYhIbRH5SkSmi8hsEbnP299WRCaLyEIReV1EaiYrhuLMnu0GWAP3Bd+yZezxcOuXWbOC9vOPPea+UH35+XDYYW7KxVGj3FANX38dO9bO4Ye7sferVXPn+aqnsGZGFSZMKDpRTGlaty6aPOrXhwUL4K23Ki4+Y0z6JfOJYBvQS1UPAboAp4jI4cBQ4FFVbQ+sBS5LYgxFXH55sN6qVdHjHTsG640bx06f6I/VD8FQDcOHu+Keyy+H7t1dMczWra6tfniydnBz+N5+O0ydWrTDVrL4cfq9iMurSZPUJjJjTPIlLRGo409PXsN7KdALeNPb/xJwZrJiKGzixGAIhDp1iv9C22MP96Xu9yc46ig3ZDLEtqX3v2B9fjPLzz5z64WTALgJWQ4+2LXR/8Mfit6jom3ZEgwSd2bKPmVjTGWT1DoCEckRkWnAKuBTYBGwTlX9kWzygZYlXHuliOSJSN7qCujZtWCBqwwGNz7OxIklxeyOn3ii265WzX1pH3igG4tno5fapk6NvS78C/8vf3HFKA8+6CqId+1yRTS77+6eHsC15w/3ZG7dGu69t9z/zBitW8PRR7t1q9w1xpRIVZP+AhoC44AjgYWh/XsDs0q7/tBDD9XyWL5c1X0Vq+bmlu0e++4b3OP5592yRw/V119Xvfba4Jj/OvLIku/Vu3fsuc89F6xXpPB7zJxZsfc2xmQ+IE8jfEenpNWQqq7zEkEPoKGI+IUyrYCkz28QnjWre/ey3eOJJ4L1y7xajVat4Pzz4eyzi54frzfyO+/A9dcH2/5MXTWTWG0e7gNgjDFhyWw11FREGnrrdXBzHs/FJYRzvdMGAu8lKwafX7I0aBDcc0/Z7nHqqcEE7z6/GWjr1rH7TznFDclckpo1gyIigA3e7A7bt8OPP5YtvsLC8waAFQ0ZY0qWzCeC5sA4EZkBfA18qqqjgduAm0VkIdAEeD7OPSqE30P2vvvc+EFlNX58UMH80Ueu+ShA27ZugpZp09ycwB9+WPpELb16ufqGNm2Cfarwv/+VPT7fxo2u0jusTp3y39cYUzUls9XQDFXtqqqdVbWTqt7v7V+sqt1Vtb2qnqeq25IVg88fE6d+OafTyclxCeDoo4Mk4O//xz9cr+REhl6oWzfox+APDb1qVdliu+ACNwfxo49CvXrB/tdeS11TVWNM5VTlexb/5S/BevgLsqyOP941Ea2oMvcaNdzSnyxm8+bSr7n2WtdB7PPPXaum446DN95wPaL9+obzznNPQv37W7t/Y0x8VforYtkyN4YQuHb91TIw7d12myvGeeABN+PXxo1uyGh/svhVq9w4QfXru6atS5bAU0+5Y8cc45bjx8feMy8v+rhCxhiTgV+NFWfWLLds3jx2ELVMcsop8O9/u3qGmjXdE0yjRrB4cfDLv3Fj1zP4X/9y9RxhdesG6xs2uERiScAYk4gq/UTgNxvN1KeBwmrUgE2b3PrVVwe9m3ftcsNWDBgQnDtnjksib7/txjnaffeKKfoyxmSfKp0I5s51xS5NmqQ7kmhatYL58926nwSKM2cOHHCAmzzGGGPKqxL8Ti67FSuKn583U/kVvYccEuxbssRV/PoGD3ZJwBhjKoq4XsiZLTc3V/Py8sp07datbijoymDnTjcHwBFHuFFKzz3XzRam6loA/fKLa56azB7IxpiqQ0SmqGpuqedV9URgjDHZKmoiqNJFQ8YYY0pnicAYY7KcJQJjjMlylgiMMSbLWSIwxpgsZ4nAGGOynCUCY4zJcpYIjDEmy1WKDmUishoo68g6ewA/VWA4FS3T44PMj9HiK79Mj9HiK5t9VLVpaSdVikRQHiKSF6VnXbpkenyQ+TFafOWX6TFafMllRUPGGJPlLBEYY0yWy4ZEMDzdAZQi0+ODzI/R4iu/TI/R4kuiKl9HYIwxJr5seCIwxhgTR5VNBCJyiojMF5GFInJ7GuPYW0TGicgcEZktIjd4+xuLyKcissBbNvL2i4g87sU9Q0S6pSjOHBGZKiKjve22IjLZi+N1Eanp7a/lbS/0jrdJQWwNReRNEZknInNFpEcGfn43ef99Z4nICBGpnc7PUEReEJFVIjIrtC/hz0xEBnrnLxCRgSmI8WHvv/MMEXlHRBqGjt3hxThfRE4O7U/K33px8YWO/V5EVET28LbT8hlWGFWtci8gB1gEtANqAtOBA9MUS3Ogm7deD/gWOBB4CLjd2387MNRb7w18CAhwODA5RXHeDLwGjPa23wD6eevPAFd769cAz3jr/YDXUxDbS8Dl3npNoGEmfX5AS+A7oE7os7s0nZ8hcDTQDZgV2pfQZwY0BhZ7y0beeqMkx3gSUN1bHxqK8UDv77gW0Nb7+85J5t96cfF5+/cGPsb1bdojnZ9hhf23SHcASflHQQ/g49D2HcAd6Y7Li+U94ERgPtDc29ccmO+t/wPoHzr/1/OSGFMrYCzQCxjt/c/8U+gP8tfP0/sD6OGtV/fOkyTG1sD7kpVC+zPp82sJfO/9sVf3PsOT0/0ZAm0Kfckm9JkB/YF/hPbHnJeMGAsdOwt41VuP+Rv2P8Nk/60XFx/wJnAIsIQgEaTtM6yIV1UtGvL/MH353r608ooAugKTgWaqutw7tAJo5q2nI/a/Af8H7PK2mwDrVHVHMTH8Gp93fL13frK0BVYDL3pFV8+JSF0y6PNT1R+AYcAyYDnuM5lC5nyGvkQ/s3T/HQ3G/comTiwpjVFE+gI/qOr0QocyIr6yqqqJIOOIyO7AW8CNqrohfEzdT4W0NN8SkdOBVao6JR3vH0F13OP506raFdiEK9b4VTo/PwCvrL0vLmm1AOoCp6QrnijS/ZmVRkSGADuAV9Mdi09EdgP+ANyd7lgqWlVNBD/gyvF8rbx9aSEiNXBJ4FVVfdvbvVJEmnvHmwOrvP2pjr0n0EdElgAjccVDjwENRaR6MTH8Gp93vAGwJonx5QP5qjrZ234Tlxgy5fMDOAH4TlVXq+p24G3c55opn6Ev0c8sLX9HInIpcDpwkZewMiXGfXHJfrr399IK+EZE9sqQ+MqsqiaCr4EOXquNmrgKuVHpCEREBHgemKuqj4QOjQL8FgQDcXUH/v5LvFYIhwPrQ4/zFU5V71DVVqraBvc5/VdVLwLGAeeWEJ8f97ne+Un7ZamqK4DvRaSjt+t4YA4Z8vl5lgGHi8hu3n9vP8aM+AxDEv3MPgZOEpFG3lPPSd6+pBGRU3DFlH1UdXOh2Pt5La7aAh2Ar0jh37qqzlTVPVW1jff3ko9rCLKCDPoMyyTdlRTJeuFq8b/FtSgYksY4jsQ9gs8Apnmv3rgy4bHAAmAM0Ng7X4AnvbhnArkpjPVYglZD7XB/aAuBfwO1vP21ve2F3vF2KYirC5DnfYbv4lpfZNTnB9wHzANmAa/gWrek7TMERuDqK7bjvrAuK8tnhiunX+i9BqUgxoW4MnX/b+WZ0PlDvBjnA6eG9iflb724+AodX0JQWZyWz7CiXtaz2BhjslxVLRoyxhgTkSUCY4zJcpYIjDEmy1kiMMaYLGeJwBhjslz10k8xJruIyE5cE8AauN6tLwOPququuBcaU0lZIjCmqC2q2gVARPbEjcpaH7gnrVEZkyRWNGRMHKq6CrgSuM7rNdpGRL4QkW+81xEAIvKyiJzpXycir4pIXxE5SES+EpFp3jj1HdL1bzGmJNahzJhCRGSjqu5eaN86oCPwC7BLVbd6X+ojVDVXRI4BblLVM0WkAa5XbAfgUeBLVX3VGwIhR1W3pPZfZEx8VjRkTGJqAH8XkS7ATmA/AFX9TESeEpGmwDnAW6q6Q0T+BwwRkVbA26q6IG2RG1MCKxoyphQi0g73pb8KuAlYiZuYJBc3K5bvZeBiYBDwAoCqvgb0AbYAH4hIr9RFbkw09kRgTBzeL/xngL+rqnrFPvmqusubfzYndPo/cYPIrVDVOd717YDFqvq4iLQGOgP/Tek/wphSWCIwpqg6IjKNoPnoK4A/hPhTwFsicgnwEW6iHABUdaWIzMWNkOo7HxggIttxs4L9KQXxG5MQqyw2poJ4M1jNxI1Rvz7d8RgTldURGFMBROQEYC7whCUBU9nYE4ExxmQ5eyIwxpgsZ4nAGGOynCUCY4zJcpYIjDEmy1kiMMaYLGeJwBhjstz/A0YGg1aqkCcEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1223066d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_price = df.mean(axis = 1)\n",
    "plot_series(mean_price, xlabel='Days', ylabel='Mean value of Microsoft Stock', color='b', legend='Mean price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to scale the dataset, this generally helps the prediction to be more accourate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1)) #other typical scale values are -1,1\n",
    "mean_price = scaler.fit_transform(np.reshape(mean_price.values, (len(mean_price),1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once, the data has been scaled, we can create the train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1056, 1)\n",
      "(453, 1)\n"
     ]
    }
   ],
   "source": [
    "train, test = train_test_split(mean_price, test_size=TEST_SIZE, shuffle=False)\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1054, 1, 1) (1054,)\n",
      "(451, 1, 1) (451,)\n"
     ]
    }
   ],
   "source": [
    "X_train, y_train = to_1dimension(train, TIME_AHEAD)\n",
    "X_test, y_test = to_1dimension(test, TIME_AHEAD)\n",
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM\n",
    "\n",
    "Now we are going to test different RNN structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_symbol(model_name='LSTM', units=10, activation='linear', time_ahead=1):\n",
    "    model = Sequential()\n",
    "    if model_name == 'LSTM':\n",
    "        model.add(LSTM(units, input_shape=(1, time_ahead)))\n",
    "    elif model_name == 'BiLSTM':\n",
    "        model.add(Bidirectional(LSTM(units), input_shape=(1, time_ahead)))\n",
    "    elif model_name == 'GRU':\n",
    "        model.add(GRU(units, input_shape=(1, time_ahead)))\n",
    "    else:\n",
    "        raise ValueError(\"Wrong model name\")\n",
    "    model.add(Dense(1))\n",
    "    model.add(Activation('linear'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train the model. For optimization, we use ADAM algorithm. In time series, adaptative optimization methods tend to get better results than traditional stochastic gradient descent methods. You can play with the different [optimizers](https://keras.io/optimizers/) available in Keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = create_symbol(model_name='LSTM', units=UNITS, time_ahead=TIME_AHEAD)\n",
    "model.compile(optimizer='adam', loss='mean_squared_error') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      " - 3s - loss: 0.0068\n",
      "Epoch 2/5\n",
      " - 2s - loss: 9.2767e-05\n",
      "Epoch 3/5\n",
      " - 2s - loss: 9.3057e-05\n",
      "Epoch 4/5\n",
      " - 2s - loss: 9.5549e-05\n",
      "Epoch 5/5\n",
      " - 3s - loss: 9.2295e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1248917f0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=EPOCHS, batch_size=BATCH_SIZE, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the model is trained, we can predict on the test set and obtain the RMSE value. The model prediction `pred_test` will have a shape of `(num_rows, TIME_AHEAD)`, having the predicted values for the next `TIME_AHEAD` days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_inv = scaler.inverse_transform([y_test])\n",
    "mean_price_inv = scaler.inverse_transform(mean_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 0.94\n"
     ]
    }
   ],
   "source": [
    "pred_test = model.predict(X_test) #pred_test.shape = (num_rows, TIME_AHEAD)\n",
    "pred_test = scaler.inverse_transform(pred_test)\n",
    "score = math.sqrt(mean_squared_error(y_test_inv[0], pred_test[:,0]))\n",
    "print('Test RMSE: %.2f' % (score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "  var render_items = [{\"docid\":\"ab5511b6-187b-4f57-bd80-d215dd265b60\",\"elementid\":\"a752eb69-2876-42f3-adcb-89c96041bbe9\",\"modelid\":\"4afc3800-1ca6-430f-9ec3-48acfb80190b\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
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     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "4afc3800-1ca6-430f-9ec3-48acfb80190b"
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     },
     "output_type": "display_data"
    },
    {
     "data": {
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dominant-baseline=\"central\" fill=\"#444444\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"start\" text-decoration=\"normal\" x=\"90\" y=\"58\">True value</text><g><path d=\" M 65 58 L 85 58\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"green\" stroke-miterlimit=\"10\" stroke-width=\"2\"/></g><text dominant-baseline=\"central\" fill=\"#444444\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"start\" text-decoration=\"normal\" x=\"90\" y=\"81\">Training set</text><g><path d=\" M 65 81 L 85 81\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"red\" stroke-miterlimit=\"10\"/></g><text dominant-baseline=\"central\" fill=\"#444444\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"start\" text-decoration=\"normal\" x=\"90\" y=\"104\">Test 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y=\"0\">800</text><g transform=\"rotate(0,0,0) translate(-322,-566) translate(385,566) rotate(0,0,0)\"><text dominant-baseline=\"hanging\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">1000</text><g transform=\"rotate(0,0,0) translate(-385,-566) translate(449,566) rotate(0,0,0)\"><text dominant-baseline=\"hanging\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">1200</text><g transform=\"rotate(0,0,0) translate(-449,-566) translate(512,566) rotate(0,0,0)\"><text dominant-baseline=\"hanging\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">1400</text><g transform=\"rotate(0,0,0) translate(-512,-566) translate(308,584) rotate(0,0,0)\"><text dominant-baseline=\"hanging\" fill=\"\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"italic\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">Days</text><g transform=\"rotate(0,0,0) translate(-308,-584)\"/></g></g></g></g></g></g></g></g></g></g><g fill=\"#444444\"><path d=\" M 45 555 L 45 28\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 39 504 L 47 504\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 39 426 L 47 426\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 39 349 L 47 349\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 39 271 L 47 271\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" 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stroke-miterlimit=\"10\"/><path d=\" M 41 101 L 45 101\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 85 L 45 85\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 70 L 45 70\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 54 L 45 54\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 39 L 45 39\" fill=\"\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><g transform=\"translate(34,504) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">30</text><g transform=\"rotate(0,0,0) translate(-34,-504) translate(34,426) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">40</text><g transform=\"rotate(0,0,0) translate(-34,-426) translate(34,349) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">50</text><g transform=\"rotate(0,0,0) translate(-34,-349) translate(34,271) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">60</text><g transform=\"rotate(0,0,0) translate(-34,-271) translate(34,194) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">70</text><g transform=\"rotate(0,0,0) translate(-34,-194) translate(34,116) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">80</text><g transform=\"rotate(0,0,0) translate(-34,-116) translate(34,39) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">90</text><g transform=\"rotate(0,0,0) translate(-34,-39) translate(16,292) rotate(-90,0,0)\"><text dominant-baseline=\"alphabetic\" fill=\"\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"italic\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">Value of Microsoft Stock</text><g transform=\"rotate(90,0,0) translate(-16,-292)\"/></g></g></g></g></g></g></g></g></g><g fill=\"#444444\"><path d=\"\" fill=\"\" stroke=\"none\"/><g transform=\"translate(45,23)\"><path fill=\"none\" stroke=\"none\"/><text dominant-baseline=\"text-after-edge\" fill=\"\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"normal\" font-weight=\"bold\" stroke=\"none\" text-anchor=\"start\" text-decoration=\"normal\" x=\"0\" y=\"0\">LSTM prediction</text></g></g></g></g></g></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_train = model.predict(X_train)\n",
    "pred_train = scaler.inverse_transform(pred_train)\n",
    "plot_series_prediction(mean_price_inv, pred_train, pred_test, time_ahead=TIME_AHEAD,\n",
    "                       title='LSTM prediction', xlabel='Days', ylabel='Value of Microsoft Stock', \n",
    "                       legend=['True value','Training set','Test prediction'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BiLSTM\n",
    "Now we can try with a Bi-directional LSTM (BiLSTM)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = create_symbol(model_name='BiLSTM', units=UNITS, time_ahead=TIME_AHEAD)\n",
    "model.compile(optimizer='adam', loss='mean_squared_error') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      " - 4s - loss: 0.0035\n",
      "Epoch 2/5\n",
      " - 3s - loss: 8.6089e-05\n",
      "Epoch 3/5\n",
      " - 3s - loss: 9.4999e-05\n",
      "Epoch 4/5\n",
      " - 3s - loss: 9.8874e-05\n",
      "Epoch 5/5\n",
      " - 3s - loss: 9.7320e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x12520a748>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=EPOCHS, batch_size=1, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 0.79\n"
     ]
    }
   ],
   "source": [
    "pred_test = model.predict(X_test)\n",
    "pred_test = scaler.inverse_transform(pred_test)\n",
    "score = math.sqrt(mean_squared_error(y_test_inv[0], pred_test[:,0]))\n",
    "print('Test RMSE: %.2f' % (score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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\",\"dtype\":\"float64\",\"shape\":[1509]}}},\"id\":\"a31e3053-e6e1-4d95-af6e-01ac51a36e2f\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"label\":{\"value\":\"Training 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Application\",\"version\":\"0.12.14\"}};\n",
       "  var render_items = [{\"docid\":\"6f53e7b1-ffca-4e19-a88f-dfb92d6611a9\",\"elementid\":\"911084cb-3e92-43b1-a21d-262a3d9f81e0\",\"modelid\":\"11c2bed0-4ba1-4ea9-bf9c-633b413d5a7a\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "11c2bed0-4ba1-4ea9-bf9c-633b413d5a7a"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:bokeh.io.export:The webdriver raised a TimeoutException while waiting for                      a 'bokeh:idle' event to signify that the layout has rendered.                      Something may have gone wrong.\n"
     ]
    },
    {
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stroke-miterlimit=\"10\" stroke-opacity=\"0.5\"/><text dominant-baseline=\"central\" fill=\"#444444\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"start\" text-decoration=\"normal\" x=\"90\" y=\"58\">True value</text><g><path d=\" M 65 58 L 85 58\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"green\" stroke-miterlimit=\"10\" stroke-width=\"2\"/></g><text dominant-baseline=\"central\" fill=\"#444444\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"start\" text-decoration=\"normal\" x=\"90\" y=\"81\">Training set</text><g><path d=\" M 65 81 L 85 81\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"red\" stroke-miterlimit=\"10\"/></g><text dominant-baseline=\"central\" fill=\"#444444\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"start\" text-decoration=\"normal\" x=\"90\" y=\"104\">Test prediction</text><g><path d=\" M 65 104 L 85 104\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"blue\" stroke-miterlimit=\"10\"/></g></g></g><g><g fill=\"#444444\"><path d=\" M 45 555 L 570 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 69 561 L 69 553\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 132 561 L 132 553\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 195 561 L 195 553\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 259 561 L 259 553\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 322 561 L 322 553\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 385 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markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 525 559 L 525 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 537 559 L 537 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 550 559 L 550 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 563 559 L 563 555\" fill=\"\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><g transform=\"translate(69,566) rotate(0,0,0)\"><text dominant-baseline=\"hanging\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">0</text><g transform=\"rotate(0,0,0) translate(-69,-566) translate(132,566) rotate(0,0,0)\"><text dominant-baseline=\"hanging\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">200</text><g transform=\"rotate(0,0,0) translate(-132,-566) translate(195,566) rotate(0,0,0)\"><text dominant-baseline=\"hanging\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">400</text><g transform=\"rotate(0,0,0) translate(-195,-566) translate(259,566) rotate(0,0,0)\"><text dominant-baseline=\"hanging\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">600</text><g transform=\"rotate(0,0,0) translate(-259,-566) translate(322,566) rotate(0,0,0)\"><text dominant-baseline=\"hanging\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">800</text><g transform=\"rotate(0,0,0) translate(-322,-566) translate(385,566) rotate(0,0,0)\"><text dominant-baseline=\"hanging\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">1000</text><g transform=\"rotate(0,0,0) translate(-385,-566) translate(449,566) rotate(0,0,0)\"><text dominant-baseline=\"hanging\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">1200</text><g transform=\"rotate(0,0,0) translate(-449,-566) translate(512,566) rotate(0,0,0)\"><text dominant-baseline=\"hanging\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">1400</text><g transform=\"rotate(0,0,0) translate(-512,-566) translate(308,584) rotate(0,0,0)\"><text dominant-baseline=\"hanging\" fill=\"\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"italic\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">Days</text><g transform=\"rotate(0,0,0) translate(-308,-584)\"/></g></g></g></g></g></g></g></g></g></g><g fill=\"#444444\"><path d=\" M 45 555 L 45 28\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 39 503 L 47 503\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 39 426 L 47 426\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 39 348 L 47 348\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 39 270 L 47 270\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 39 192 L 47 192\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 39 114 L 47 114\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 39 36 L 47 36\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 519 L 45 519\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 535 L 45 535\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 550 L 45 550\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 503 L 45 503\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 488 L 45 488\" fill=\"none\" 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paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 348 L 45 348\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 332 L 45 332\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 316 L 45 316\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 301 L 45 301\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 285 L 45 285\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 270 L 45 270\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 254 L 45 254\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 238 L 45 238\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 223 L 45 223\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 207 L 45 207\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 192 L 45 192\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 176 L 45 176\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 161 L 45 161\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 145 L 45 145\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 129 L 45 129\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 114 L 45 114\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 98 L 45 98\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 83 L 45 83\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 67 L 45 67\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 51 L 45 51\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 41 36 L 45 36\" fill=\"\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><g transform=\"translate(34,503) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">30</text><g transform=\"rotate(0,0,0) translate(-34,-503) translate(34,426) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">40</text><g transform=\"rotate(0,0,0) translate(-34,-426) translate(34,348) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">50</text><g transform=\"rotate(0,0,0) translate(-34,-348) translate(34,270) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">60</text><g transform=\"rotate(0,0,0) translate(-34,-270) translate(34,192) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">70</text><g transform=\"rotate(0,0,0) translate(-34,-192) translate(34,114) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">80</text><g transform=\"rotate(0,0,0) translate(-34,-114) translate(34,36) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">90</text><g transform=\"rotate(0,0,0) translate(-34,-36) translate(16,292) rotate(-90,0,0)\"><text dominant-baseline=\"alphabetic\" fill=\"\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"italic\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">Value of Microsoft Stock</text><g transform=\"rotate(90,0,0) translate(-16,-292)\"/></g></g></g></g></g></g></g></g></g><g fill=\"#444444\"><path d=\"\" fill=\"\" stroke=\"none\"/><g transform=\"translate(45,23)\"><path fill=\"none\" stroke=\"none\"/><text dominant-baseline=\"text-after-edge\" fill=\"\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"normal\" font-weight=\"bold\" stroke=\"none\" text-anchor=\"start\" text-decoration=\"normal\" x=\"0\" y=\"0\">BiLSTM prediction</text></g></g></g></g></g></svg>"
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_train = model.predict(X_train)\n",
    "pred_train = scaler.inverse_transform(pred_train)\n",
    "plot_series_prediction(mean_price_inv, pred_train, pred_test, time_ahead=TIME_AHEAD,\n",
    "                       title='BiLSTM prediction', xlabel='Days', ylabel='Value of Microsoft Stock', \n",
    "                       legend=['True value','Training set','Test prediction'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GRU\n",
    "Finally, we are going to try with a [Gated Recurrent Unit](https://en.wikipedia.org/wiki/Gated_recurrent_unit) (GRU), which is a simplified version of LSTM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = create_symbol(model_name='BiLSTM', units=UNITS, time_ahead=TIME_AHEAD)\n",
    "model.compile(optimizer='adam', loss='mean_squared_error') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      " - 4s - loss: 0.0040\n",
      "Epoch 2/5\n",
      " - 3s - loss: 8.6400e-05\n",
      "Epoch 3/5\n",
      " - 3s - loss: 9.1221e-05\n",
      "Epoch 4/5\n",
      " - 3s - loss: 9.6361e-05\n",
      "Epoch 5/5\n",
      " - 3s - loss: 9.6260e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x12520a8d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=EPOCHS, batch_size=1, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 0.62\n"
     ]
    }
   ],
   "source": [
    "pred_test = model.predict(X_test)\n",
    "pred_test = scaler.inverse_transform(pred_test)\n",
    "score = math.sqrt(mean_squared_error(y_test_inv[0], pred_test[:,0]))\n",
    "print('Test RMSE: %.2f' % (score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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prediction\"},\"renderers\":[{\"id\":\"8ec64d0d-d43c-48dd-9974-3b3d2b8a1e13\",\"type\":\"GlyphRenderer\"}]},\"id\":\"f939bd9f-a9f2-44b1-a418-de06a4886067\",\"type\":\"LegendItem\"},{\"attributes\":{\"callback\":null},\"id\":\"66499f39-276f-45b7-9a46-4659c5c05f42\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"7299fd9b-a0c4-43b2-b7a4-8381a5caee4a\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"30c2d351-352d-41d5-b15b-0c95280c5200\",\"type\":\"LinearScale\"},{\"attributes\":{\"line_color\":\"red\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"bb30b025-75a1-4771-9735-7458b8abd84c\",\"type\":\"Line\"},{\"attributes\":{\"overlay\":{\"id\":\"f0d139e0-334a-4a8c-976e-21855b8917fb\",\"type\":\"BoxAnnotation\"}},\"id\":\"51336cfb-d5e1-4a89-87ce-ac41475ca5f8\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"486075ee-4a75-4a75-acd5-6df91d29de52\",\"type\":\"Line\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"dfb8d063-2253-4028-8b62-f77dac18ea91\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"02367c68-af2d-4a5e-ad09-7b7ed80cd2b8\",\"type\":\"BasicTicker\"}},\"id\":\"c9de9f9e-570a-4871-bf2a-52362292d1ed\",\"type\":\"Grid\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"f0d139e0-334a-4a8c-976e-21855b8917fb\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"77ca1e67-afc8-4687-b4d3-2b30b48e4dfa\",\"type\":\"SaveTool\"}],\"root_ids\":[\"dfb8d063-2253-4028-8b62-f77dac18ea91\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.14\"}};\n",
       "  var render_items = [{\"docid\":\"8f2c6453-9eac-4f22-b808-7febf42d4128\",\"elementid\":\"f43d8e25-9544-448d-a5d1-b8738d93ddd2\",\"modelid\":\"dfb8d063-2253-4028-8b62-f77dac18ea91\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "dfb8d063-2253-4028-8b62-f77dac18ea91"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:bokeh.io.export:The webdriver raised a TimeoutException while waiting for                      a 'bokeh:idle' event to signify that the layout has rendered.                      Something may have gone wrong.\n"
     ]
    },
    {
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L 531.8941403424161 115.18718320561163 L 532.2106341933927 117.38453236125781 L 532.5271280443694 113.76921130027551 L 532.843621895346 70.25328108037445 L 533.1601157463226 76.12808775163796 L 533.4766095972992 76.16777103329377 L 533.7931034482758 79.1670106474511 L 534.1095972992524 76.22732597338836 L 534.426091150229 73.884631545596 L 534.7425850012056 71.32454957941422 L 535.0590788521822 70.49126069986528 L 535.3755727031588 71.93967046268926 L 535.6920665541355 75.45299168171528 L 536.0085604051121 76.20745431494947 L 536.3250542560887 76.20745431494947 L 536.6415481070653 76.92241377219364 L 536.9580419580419 80.49829169240763 L 537.2745358090185 80.83616992108932 L 537.5910296599951 84.37458586285675 L 537.9075235109717 86.58220104144266 L 538.2240173619483 80.08104690041455 L 538.5405112129249 78.41218780288568 L 538.8570050639015 81.19385977298782 L 539.1734989148782 77.2600518599877 L 539.4899927658548 70.17391451706305 L 539.8064866168314 73.90450320403477 L 540.122980467808 75.11541362914295 L 540.4394743187846 74.42062600644704 L 540.7559681697612 84.87167749993637 L 541.0724620207378 92.91093402526155 L 541.3889558717144 88.3130164879409 L 541.705449722691 86.46279098514424 L 542.0219435736676 74.79770723500951 L 542.3384374246442 68.23021417381551 L 542.6549312756209 62.26457421186342 L 542.9714251265975 61.78927536032245 L 543.2879189775741 65.23667790573575 L 543.6044128285507 58.185721202826585 L 543.9209066795273 51.95454545454561 L 544.2374005305039 58.73990633590404 L 544.5538943814805 60.858369210013734 L 544.8703882324571 60.22475747840281 L 545.1868820834337 63.37402511201208 L 545.5033759344103 63.750445953133976 L 545.819869785387 61.19510676947982\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"blue\" stroke-miterlimit=\"10\"/></g></g><g><g/><g><path d=\" M 55 38 L 183.71875 38 L 183.71875 124 L 55 124 L 55 38 Z\" fill=\"#ffffff\" fill-opacity=\"0.95\" paint-order=\"fill stroke markers\" stroke=\"#e5e5e5\" stroke-miterlimit=\"10\" stroke-opacity=\"0.5\"/><text dominant-baseline=\"central\" fill=\"#444444\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"start\" text-decoration=\"normal\" x=\"90\" y=\"58\">True value</text><g><path d=\" M 65 58 L 85 58\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"green\" stroke-miterlimit=\"10\" stroke-width=\"2\"/></g><text dominant-baseline=\"central\" fill=\"#444444\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"start\" text-decoration=\"normal\" x=\"90\" y=\"81\">Training set</text><g><path d=\" M 65 81 L 85 81\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"red\" stroke-miterlimit=\"10\"/></g><text dominant-baseline=\"central\" fill=\"#444444\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"start\" text-decoration=\"normal\" x=\"90\" y=\"104\">Test prediction</text><g><path d=\" M 65 104 L 85 104\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"blue\" stroke-miterlimit=\"10\"/></g></g></g><g><g fill=\"#444444\"><path d=\" M 45 555 L 570 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 69 561 L 69 553\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 132 561 L 132 553\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 195 561 L 195 553\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 259 561 L 259 553\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 322 561 L 322 553\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 385 561 L 385 553\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 449 561 L 449 553\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 512 561 L 512 553\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 56 559 L 56 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 69 559 L 69 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 82 559 L 82 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 94 559 L 94 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 107 559 L 107 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 120 559 L 120 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 132 559 L 132 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 145 559 L 145 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 157 559 L 157 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 170 559 L 170 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 183 559 L 183 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 195 559 L 195 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 208 559 L 208 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 221 559 L 221 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 233 559 L 233 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 246 559 L 246 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 259 559 L 259 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 271 559 L 271 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 284 559 L 284 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 297 559 L 297 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" stroke-miterlimit=\"10\"/><path d=\" M 309 559 L 309 555\" fill=\"none\" paint-order=\"fill stroke markers\" stroke=\"black\" 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dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">70</text><g transform=\"rotate(0,0,0) translate(-34,-188) translate(34,110) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">80</text><g transform=\"rotate(0,0,0) translate(-34,-110) translate(34,31) rotate(0,0,0)\"><text dominant-baseline=\"central\" fill=\"\" font-family=\"helvetica\" font-size=\"8pt\" font-style=\"normal\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"end\" text-decoration=\"normal\" x=\"0\" y=\"0\">90</text><g transform=\"rotate(0,0,0) translate(-34,-31) translate(16,292) rotate(-90,0,0)\"><text dominant-baseline=\"alphabetic\" fill=\"\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"italic\" font-weight=\"normal\" stroke=\"none\" text-anchor=\"middle\" text-decoration=\"normal\" x=\"0\" y=\"0\">Value of Microsoft Stock</text><g transform=\"rotate(90,0,0) translate(-16,-292)\"/></g></g></g></g></g></g></g></g></g><g fill=\"#444444\"><path d=\"\" fill=\"\" stroke=\"none\"/><g transform=\"translate(45,23)\"><path fill=\"none\" stroke=\"none\"/><text dominant-baseline=\"text-after-edge\" fill=\"\" font-family=\"helvetica\" font-size=\"10pt\" font-style=\"normal\" font-weight=\"bold\" stroke=\"none\" text-anchor=\"start\" text-decoration=\"normal\" x=\"0\" y=\"0\">GRU prediction</text></g></g></g></g></g></svg>"
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
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    }
   ],
   "source": [
    "pred_train = model.predict(X_train)\n",
    "pred_train = scaler.inverse_transform(pred_train)\n",
    "plot_series_prediction(mean_price_inv, pred_train, pred_test, time_ahead=TIME_AHEAD,\n",
    "                       title='GRU prediction', xlabel='Days', ylabel='Value of Microsoft Stock', \n",
    "                       legend=['True value','Training set','Test prediction'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "In this tutorial we have seen how a LSTM (or other variants) can be used to predict the stock price given a temporal series of values. "
   ]
  }
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